International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1080
An Efficient Technique for Optimality Measurement of Approximation Algorithms
Статья научная
Many algorithms have been proposed for the solution of the minimum vertex cover (MVC) problem, but the researchers are unable to find the optimality of an approximation algorithm. In this paper, we have proposed a method to evaluate that either the result returned by an approximation algorithm for the minimum vertex cover problem is optimal or not. The proposed method is tested on three algorithms, i.e., maximum degree greedy (MDG) algorithm, modified vertex support algorithm (MVSA) and clever steady strategy algorithm (CSSA). The proposed method provides an opportunity to test the optimality of an approximation algorithm for MVC problem with low computation complexity. The proposed method has performed well during experimentation, and its results brighten the path of successful implementation of the method for the evaluation of approximation algorithms for the minimum vertex cover (MVC) problem. The testing of the proposed method was carried out on small graph instances. The proposed method has resolved the problem to test the optimality of the approximation algorithm for the minimum vertex cover problem. This technique has digitized the process of finding out the accuracy of the optimal solution returned by approximation algorithms for MVC.
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An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment
Статья научная
Cloud is a collection of heterogeneous resources and requirements of these resources can change dynamically. Cloud providers are always interested in maximizing the resources utilization and the associated revenues, by trimming down energy consumption and operational expenses, while on the other hand cloud users are interested in minimizing response time and optimizing overall application throughput. In cloud environment to allocate the resources with minimum overhead time along with efficient utilization of available resources is very challenging task. The resources in cloud datacenter are allocated using a virtual machine (VM) scheduling technique. So there is a need of an efficient VM scheduling technique to maximize system performance and cost saving. In this paper two dynamic virtual machine scheduling techniques i.e. Best fit and Worst fit are proposed for reducing the response time along with efficient and balanced resource utilization. The proposed algorithms removes the limitations of the previously proposed Novel Vector based algorithm and minimizes the response time complexity in order of O(logn) and O(1) using Best Fit and Worst Fit strategies respectively.
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An Efficient and Effective New Generation Objective Quality Model for Mobile Applications
Статья научная
Recent proliferation of mobile market has swiftly increased the competition in mobile software market, new technology and new devices are emerging at phenomenal speed. As the number of mobile applications is increasing at daily rate, quality is becoming major issue. So mobile software organization need some quality model as guideline to improve and maintain quality of application under development. Mobile application mainly depends on user response and user acceptance so they need maintainability, usability, suitability etc. This research paper presents mobile application quality model focusing on key quality characteristics mainly extracted from ISO 9126 quality model, which effect quality of mobile applications. Furthermore some responsibilities of QA team in mobile application development are also discussed and lastly focused on the issue of ‘tracking the quality of mobile applications after deployment’.
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An Efficient and Objective Generalized Comparison technique for Software Quality Models
Статья научная
To scrutinize the uniqueness of software quality model it is crucial to compare it with existing ones. Quality is generally apprehended in a model that illustrates the features and their interactions. Numerous models for measuring quality of software processes have been recommended to assess particular type of software products. Numerous methodologies and practices have been suggested to perform the specific or general scope based comparisons among eminent models. These comparisons are leak. The Suggested comparison lacks the clear differentiation and in depth analysis. Consequently, a prescribed method of comparison among software quality models has been defined. The suggested technique is applied on an inclusive comparison among renowned software quality models. The consequence of suggested technique demonstrates the power and faintness of quality models.
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Статья научная
Clustering diabetic patients with comorbidity patterns are necessary to learn relationships between diabetes patients’ clinical profiles and as an essential pre-processing stage for analysis tasks, like classification and categorization. Nevertheless, the heterogeneity of these data makes traditional clustering methods more difficult to apply, necessitating the development of novel clustering algorithms. In this paper, we recommend an effective and scalable clustering technique suitable for datasets made up of attributes which are atomic and set-valued. In these datasets, each record corresponds to a different diagnosis detail of a diabetic patient based on his or her hospital visit, where diagnosis details in each record are represented using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Our proposed technique involves three main stages. In the first stage, we selected the top-k diabetes-specific comorbidities patterns. In the second stage, we ensured that the co-occurring conditions in the selected top-k diabetes-specific comorbidities patterns really co-occur together or not using topic modeling and in the last stage, we constructed high quality clusters efficiently using average linkage agglomerative clustering with cosine similarity. Also, based on silhouette analysis, we assessed the efficiency and effectiveness of our proposed technique using a large, freely available MIMIC dataset (MIMIC-III and MIMIC-IV), comprised of over 14,222 and 68,118 distinct records, respectively. Our findings reveal that our technique finds clusters that: (i) preserve interrelations between demographics (age, gender) and diagnosis codes (ICD-9-CM codes), and (ii) are well-separated and compact. Finally, the founded clusters are beneficial for numerous investigative tasks like query answering, visualization, anonymization, classification etc.
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Статья научная
Learning styles vary according to the individual and this diversity is fundamental in terms of teaching as curricula must respond adaptively to the various learning styles of pupils. This study conducts an analysis of an Arabic form of the Index of Learning Styles (ILS), a 44-item questionnaire designed to determine learning styles using the Felder-Silverman learning style model. This study focuses on the interpretation of data derived from the Arabic form of the Index of Learning Styles (ILS) to establish correlations between the learning styles of 1024 female students drawn from two specific departments at the King Abdul-Aziz University in Saudi Arabia. The findings, generated by Multiple Correspondence Analysis and cross-validated by correlation analysis, demonstrate a definite link between certain learning styles from opposing dimensions that are considered to be contradictory within the same dimension of learning. The validity and reliability of the Arabic scale was established and compared to the examples reported in the literature. Findings show comparable reliability and factor analysis supports the interdependencies between dimensions and perhaps the constructs they intend to assess. The results of this paper have implications for the design of e-learning tools, materials and sessions in order to adapt to the relationships between learning styles and have a positive impact on the learners themselves and their learning experience.
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Статья научная
Learning practices commenced to shift from face-to-face offline class learning to online classes with technological networks specifically on sudden COVID-19 crises. . This sort of variation in their learning method sparks question about students' perception of the new learning system. The objective of the study was to compare English language learning, between online classes and Offline-classes and it explicates different students' perceptions of such learning practices regarding the benefits, improvements, and drawbacks of online and offline modes. The research approach of study, proceeds with a quantitative study, using statistical analysis through questionnaire distribution. The participants of the study were the school students, obtained from Government and private schools in Telangana. The quality of the study stands outstanding in addressing the effectiveness of blended learning both online and offline learning and aids to study nature of the approach if integration of learning modes including face-to face and online learning incorporated and the consideration to improvise qualities learning experiences of students. With those aspects, the research is significant to prove the preference of students to elucidate that offline classroom learning is more preferable than online English learning. The value of the research is recognised that it aids the educators, leadership authorities and researchers to understand parameters leading to efficient learning practices, enhanced collaborative student performance outcomes assisting to select the appropriate technologies in case of any pandemic crisis and to inhibit collaborative learning in and out of classroom. The most general obstacles faced by students in online English learning are materials insufficiency, lack of communicative skills training, lacking reading activities participation, absence of interaction, the inability of queries or doubts clarification, and exercise exposure are addressed by the analysis outcomes. The comparative perception outcomes explicated that Offline English language learning stands out as more efficient than the online learning method.
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An Energy-Efficient and Robust Voltage Level Converter for Nanoelectronics
Статья научная
Low-power design has recently become very important especially in nanoelectronic VLSI circuits and systems. Functioning of circuits at ultra-low voltages leads to lower power consumption per operation. An efficient method is to separate the logic blocks based on their performance requirement and applying a specific supply voltage for each block. In order to prevent an enormous static current in these multi-VDD circuits, voltage level converters are essential. This study presents an energy-efficient and robust single-supply level converter (SSLC) based on multi-threshold carbon nanotube FETs (CNTFETs). Unique characteristics of the CNTFET device and transistor stacking are utilized suitably to reduce the power and energy consumption of the proposed LC. The results of the extensive simulations, conducted using 32nm CNTFET technology of Stanford University indicate the superiority of the proposed design in terms energy-efficiency and robustness to process, voltage and temperature variations, as compared to the other conventional and state-of-the-art LC circuits, previously presented in the literature. The results demonstrate almost on average 35%, 55%, 90% and 68% improvements in terms of delay, total power, static power and energy consumption, respectively.
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An Enhanced Action Research Approach for Managing Risks in Software Process Improvement
Статья научная
Managing risks in Software Process Improvement (SPI) is a key point of software success. A software risk is considered as an essential characteristic of software development process which if ignored will increase the chance of project failure. For this purpose different risk management approaches are developed. These approaches lead to the identification, assessment and control of risk occurrence in software projects. Collaborative Practice Research (CPR) is one of the action research approaches for managing risk in SPI. In this approach the focus is on gathering information regarding SPI and acknowledging risk management in process development by developing risk assessment strategies and models. The main challenge of this action research approach is to validate the developed risk approach. This paper has a critical review on the existing research approach i.e. CPR. It also provides an enhanced form of CPR which modifies the current CPR approach by including a risk validation activity.
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An Enhanced Digital Image Watermarking Scheme for Medical Images using Neural Network, DWT and RSA
Статья научная
Image watermarking is the process of the hiding the one image into other image for the copyright protection. The process of watermarking must be done in this way that the pixels of the original image must remain in its original HD form. A lot of work has been done in this context in previous years but some techniques have their own applications, drawbacks as well as advantages. So, this paper will utilize three techniques i.e. Discreet Wavelet Transform (DWT), Neural Network (NN) and RSA encryption for image watermarking. In the end the performance of the proposed technique will be measured on the basis of PSNR, MSE, BCR, BER and NCC in MATLAB R2010a environment.
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An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Статья научная
A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders' blurriness. Clusters' quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.
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An Evaluation of the Critical Factors Affecting the Efficiency of Some Sorting Techniques
Статья научная
Sorting allows information or data to be put into a meaningful order. As efficiency is a major concern of computing, data are sorted in order to gain the efficiency in retrieving or searching tasks. The factors affecting the efficiency of shell, Heap, Bubble, Quick and Merge sorting techniques in terms of running time, memory usage and the number of exchanges were investigated. Experiment was conducted for the decision variables generated from algorithms implemented in Java programming and factor analysis by principal components of the obtained experimental data was carried out in order to estimate the contribution of each factor to the success of the sorting algorithms. Further statistical analysis was carried out to generate eigenvalue of the extracted factor and hence, a system of linear equations which was used to estimate the assessment of each factor of the sorting techniques was proposed. The study revealed that the main factor affecting these sorting techniques was time taken to sort. It contributed 97.842%, 97.693%, 89.351%, 98.336% and 90.480% for Bubble sort, Heap sort, Merge sort, Quick sort and Shell sort respectively. The number of swap came second contributing 1.587% for Bubble sort, 2.305% for Heap sort, 10.63% for Merge sort, 1.643% for Quick sort and 9.514% for Shell sort. The memory used was the least of the factors contributing negligible percentage for the five sorting techniques. It contributed 0.571% for Bubble sort, 0.002% for Heap sort, 0.011% for Merge sort, 0.021% for Quick sort and 0.006% for Shell sort.
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An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
Статья научная
A new neuro-fuzzy system's architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers' location of membership functions and the receptive field's parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results proves the effectiveness of the developed architecture and the learning procedure.
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An Experimental Study on College Teacher's Adoption of Instructional Technology
Статья научная
Instructional technology can make teachers do their jobs easier, better, faster and more effectively. Students can also benefit from its application. However, some college teachers do not adopt instructional technologies in their teaching as we expected. They like to teach the way they were taught as students before. Why and what factors really influence their adoption of instructional technology? This study offered a model suggestiong instructional technology adoption by college teachers depends on: the student, the teacher, the technology and the surroundings. An experiment was designed to verify the model. Samples were selected from teachers at a mid-sized university. Experimental data was collected by interviewing fifteen teachers (samples). Those interviewed represented five high-level users, five medium-level users, and five low-level users of instructional technology. Quantitative methods such as frequency counting were used to analyze and sort the data. Finally, conclusions can be drawn that different components in the model had different influential degree to the different levels of users of instructional technology.
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An Identification of Better Engineering College with Conflicting Criteria using Adaptive TOPSIS
Статья научная
Students like to find better engineering college for their higher education. It is very challenging to find the better engineering college with conflicting criteria. In this research, the criterion such as academic reputation and achievements, infrastructure, fees structure, location, quality of the faculty, research facilities and other criterion are considered to find the better engineering college. Multi Criteria Decision Making (MCDM) is the most well known branch of decision making under the presence of conflicting criteria. TOPSIS is one of the MCDM technique widely applied to solve the problems which involves many number of criteria. In this research, TOPSIS is Adaptive and applied to find better engineering college. To evaluate the proposed methodology the parameters such as time complexity, space complexity, sensitivity analysis and rank reversal are considered. In this comparative analysis, better results are obtained for Adaptive TOPSIS compared to COPRAS.
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An Identity-based Blind Signature Approach for E-voting System
Статья научная
Electronic voting is a voting process using electronic mean that allows voters to cast their secret and secure vote over an unsecured channel. Many forward-thinking countries are adopting the electronic voting system to upgrade their election process. Since E-voting system is more complex so it requires more security as compared to the postal voting system. One of the fine tool to provide the voter anonymity is the blind signature scheme. Many blind signature proposals based on traditional public key cryptosystem have been discussed, however, they get the worst of certificate and public key management. In this sense, the objective of the paper is twofold. Firstly, we proposed a blind signature scheme using the identity-based cryptosystem. Proposed scheme uses the combination of Bolyreva’s blind signature scheme and Cha-Chaon’s Identity-based signature. Secondly, we show that proposed scheme is more suitable for E-voting system as compared with others ID-based blind signature scheme.
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An Improved Chaotic Bat Algorithm for Solving Integer Programming Problems
Статья научная
Bat Algorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of a Bat Meta-heuristic Algorithm, (IBACH), for solving integer programming problems. The proposed algorithm uses chaotic behaviour to generate a candidate solution in behaviors similar to acoustic monophony. Numerical results show that the IBACH is able to obtain the optimal results in comparison to traditional methods (branch and bound), particle swarm optimization algorithm (PSO), standard Bat algorithm and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm (exact solution method).
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An Improved Method of Geometric Hashing Pattern Recognition
Статья научная
Geometric hashing (GH) is a general model-based recognition scheme. GH is widely used in the industrial products assembly and inspection tasks. The aim of this study is to speed up the geometric hashing pattern recognition method for the purpose of real-time object detection applications. In our method, a pattern is decomposed into some sub-patterns to reduce the data number in hash table bins. In addition, the sub-patterns are recorded in a plurality of hash tables. Finally we improve the recognition performance by combining with image pyramid and edge direction information. To confirm the validity of our proposed method, we make a complexity analysis, and apply our method to some images. Both complexity analysis and experiment evaluations have demonstrated the efficiency of this technique.
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An Improved Text Clustering Method based on Hybrid Model
Статья научная
According to the high-dimensional sparse features on the storage of textual document, and defects existing in the clustering methods or the hybrid methods which have already been studied by now and some other problems. So an improved text clustering method based on hybrid model, that is a text clustering approach (short for TGSOM-FS-FKM) based on tree-structured growing self-organizing maps (TGSOM) and Fuzzy K-Means (FKM) is proposed. The method has optimized the clustering result through three times of clustering. It firstly makes preprocess of texts, and filters the majority of noisy words by using an unsupervised feature selection method. Then it used TGSOM to execute the first clustering to get a rough classification of texts, and to get the initial clustering number and each text’s category. And then introduced LSA theory to improve the precision of clustering and reduce the dimension of the feature vector. After that, it used TGSOM to execute the second clustering to get more precise clustering results, and used supervised feature selection method to select feature items. Finally, it used FKM to cluster the result set. In the experiment, it remained the same number of feature items and experimental results indicate that TGSOM-FS-FKM clustering excels to other clustering method such as DSOM-FS-FCM, and the precision is better than DSOM-FCM, DFKCN and FDMFC clustering.
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An Improved, Efficient and Cost Effective Software Inspection Meeting Process
Статья научная
Normally, the inspection process is seemed to be just finding defects in software during software development process lifecycle. Software inspection is considered as a most cost effective technique, but if these defects are not properly corrected or handled it would cost you more than double later in the project. This paper focus on the last phase of inspection meeting process showing the importance of Follow-Up Stage in software inspection meeting process. This paper also suggests a set of activities that should be performed during the Rework and Follow-Up Stages so to get inspection meeting results productive and efficient. In this paper we focus on the over the shoulder reviews so to ensure the software quality having less impact on the total software cost.
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